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- http://urbansed.weebly.com/
- Stochastic Adaptive Neural Architecture Search. Contribute to TomVeniat/SANAS development by creating an account on GitHub.
- In this tutorial, I will first teach you how to build a recurrent neural network (RNN) with a single layer, consisting of one single neuron, with PyTorch and Google Colab. I will also show you how…
- https://github.com/Pinafore/ml-hw/blob/master/logreg/assign.md
- http://people.ict.usc.edu/~roemmele/
- scipy.misc.logsumexp
- Keras-GAN - Keras implementations of Generative Adversarial Networks.
- Educational resources for machine learning
- https://upc-mai-dl.github.io/
- Submanifold sparse convolutional networks
- Learning Complex Basis Functions for Invariant Signal Representations with the Complex Autoencoder - SonyCSLParis/cae-invar
- Restoring ancient text using deep learning: a case study on Greek epigraphy. - sommerschield/ancient-text-restoration
- Deep learning course CE7454, 2019. Contribute to xbresson/CE7454_2019 development by creating an account on GitHub.
- Virtual Adversarial Training (VAT) implementation for PyTorch - lyakaap/VAT-pytorch
- A PyTorch Implementation of End-to-End Models for Speech-to-Text - awni/speech
- Generative Models
- An official PyTorch implementation of “Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation” (NeurIPS 2019) by Risto Vuorio*, Shao-Hua Sun*, Hexiang Hu, and Joseph J. Lim - shaohua0116/MMAML-Classification
- Bayesian Deep Learning Benchmarks. Contribute to OATML/bdl-benchmarks development by creating an account on GitHub.
- Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Fall 2019 Notebook: http://deeplearning.cs.cmu.edu/document/recitation/recitation...
- Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Fall 2019 For more information, please visit: http://deeplearning.cs.cmu.edu/ Con...
Publikationen
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- Information, 14 (2): 137 (Februar 2023)
- Transactions of the International Society for Music Information Retrieval, 6 (1): 89–102 (2023)
- (2021)cite arxiv:2110.05838Comment: Under review.
- Frontiers in Neuroscience, (März 2020)
- (2020)cite arxiv:2010.10504Comment: 11 pages, 3 figures, 5 tables. Submitted to NeurIPS SAS 2020 Workshop.
